The impact of the spatial resolution of wind data on multi-decadal wind power forecasts in Germany

TL;DR

This study evaluates how the spatial resolution of global climate models affects multi-decadal wind power forecasts in Germany, highlighting the importance of model choice and the limitations of resolution improvements.

Contribution

It provides a comprehensive analysis of the impact of GCM spatial resolution on wind speed predictions for wind power forecasting in Germany, emphasizing model selection and data correction.

Findings

Higher resolution models better capture extreme wind speeds.

Model choice significantly influences wind speed outputs.

High-resolution models do not always improve overall wind speed distribution predictions.

Abstract

Accurate multi-decadal wind power predictions are crucial for sustainable energy transitions but are challenged by the coarse spatial resolution of global climate models (GCMs). This study examines the impact of spatial resolution on wind power forecasts by analyzing historical wind speed outputs from ten CMIP6 GCMs in Germany, using ERA5 reanalysis as a reference. Results show that the choice of GCM is the primary influence on wind speed output, with higher resolution models partly, but not consistently, improving predictions. While high-resolution models better capture extreme wind speeds, they do not systematically improve the prediction of the whole wind speed distribution. The data set MPI-ESM1-2-HR (MPI-HR) was found to represent the wind speed distribution particularly faithfully, while the MIROC6 (JAP) data set showed substantial underestimation for the German region compared to ERA5. These findings underscore the complexity of wind speed modeling for power predictions and emphasize the need for careful GCM selection and appropriate downscaling and bias correction methods.